# SIB200 Base Model (Cross-Lingual)

This model was trained on the SIB200 dataset using random data selection with cross-lingual training.

## Training Parameters

- **Dataset**: SIB200
- **Mode**: Base
- **Selection Method**: Random
- **Cross Lingual**: true
- **Train Size**: 700 examples
- **Epochs**: 20
- **Batch Size**: 8
- **Effective Batch Size**: 32 (batch_size * gradient_accumulation_steps)
- **Learning Rate**: 8e-06
- **Patience**: 8
- **Max Length**: 192
- **Gradient Accumulation Steps**: 4
- **Warmup Ratio**: 0.1
- **Weight Decay**: 0.01
- **Optimizer**: AdamW
- **Scheduler**: cosine_with_warmup
- **Random Seed**: 42

## Performance

- **Overall Accuracy**: 70.71%
- **Overall Loss**: 0.0212

### Language-Specific Performance
- **English (EN)**: 83.84%
- **German (DE)**: 88.89%
- **Arabic (AR)**: 24.24%
- **Spanish (ES)**: 87.88%
- **Hindi (HI)**: 74.75%
- **Swahili (SW)**: 64.65%

## Model Information

- **Base Model**: bert-base-multilingual-cased
- **Task**: Topic Classification
- **Languages**: 6 languages (EN, DE, AR, ES, HI, SW)